BUS708 Statistics and Data Analysis
Inferential Statistics Report
Assignment 2 (Assessment 4) – Individual Word Report – Trimester 1, 2020
1 OVERVIEW OF THE ASSIGNMENT
This assignment will test your skill to present and summarise data as well as to make basic statistical
inferences in a business context. You will use the results and any feedback given in Assignment 1
(Assessment 3, Excel Report) and produce a single report in a word document. You will need to
construct interval estimates, perform suitable hypothesis tests and regression analysis and make
conclusion and suggestion for management action.
Your report should be written in a word document and should be submitted to Turnitin following the
equirement explained below.
2 TASK DESCRIPTION
There are two datasets involved in this assignment: Dataset 1 and Dataset 2, which are the same
datasets used in Assignment 1 (Excel Report). All data processing should be performed in Excel or
Statkey (http:
www.lock5stat.com/StatKey). Specific instruction as to which tools should be used
for each section will be given during tutorials.
Your tasks are to answer the following research questions given in Section 2 to Section 6 below using
dataset 1 or dataset 2 as indicated in each section. To answer each question, you will need to first
present the relevant numerical summary (summary statistics) and graphical display and perform
suitable statistical analysis to provide a conclusion.
Your tasks are described below.
1. Section 1: Introduction
Provide a
ief and clear introduction about the report (e.g. the objective of the
eport, the datasets involved, etc.). Find relevant articles (minimum one article,
maximum 3 articles) and write a proper literature review which includes in-text
citation.
2. Section 2: Is Flat makes up about 50% of Dwelling Type?
Using Dataset 1, first provide both numerical summary as well as graphical display
that easily shows the proportions of dwelling type.
Then construct a 95% confidence interval of the population proportion of dwelling
type.
Finally, answer the research question using the confidence interval.
http:
www.lock5stat.com/StatKey
3. Section 3: Is the Average Weekly Rent of Flats in Sydney More than $800?
Using Dataset 1, first describe the weekly rent distribution of Flats in Sydney
(postcode XXXXXXXXXXYou need to provide numerical summary (sample size, mean,
standard deviation and median) as well as graphical display which shows any
outliers.
Then perform a suitable hypothesis test to answer the research question above at
5% level of significance.
4. Section 4: Is there a difference in Weekly Rent among five different postcodes?
Using Dataset 1, describe the distribution of Weekly Rent for each of the following
postcodes: 2000 (Sydney), 2017 (Waterloo), 2145 (Westmead), 2150 (Pa
amatta),
and 2170 (Liverpool). You need to provide both numerical summary as well as
graphical display which shows any outliers.
Then perform a suitable hypothesis test to answer the research question above. Use
a 5% significance level.
5. Section 5: Can we predict the Weekly Rent for flats in Sydney using the Number of
Bedrooms?
Using Dataset 1, first describe the relationship between the weekly rent and the
number of bedrooms for flats in Sydney. You need to provide both numerical
summary as well as graphical display.
Then interpret the co
elation coefficient, coefficient determination and the
elevant p-values and use them to answer the research question.
6. Section 6: Is there any relationship between country of origin and subu
where
international students live?
Using Dataset 2, describe the relationship between the country of origin of an
international student and the subu
they cu
ently live in. You need to provide both
numerical summary and graphical display.
Then perform a suitable hypothesis test to answer the research question above. Use
a 5% significance level.
7. Section 7: Conclusion
Write a summary of all the findings in the previous sections and then write
concluding statements that would benefit a stake holder (e.g. an investor or a
enter) to take management action. Finally, suggest further research by discussing
an interesting topic or a research question that can be further explored related to
the datasets.
3 SUBMISSION REQUIREMENT
Deadline to submit the report: Monday, 1st June 2020, 23:59 (11:59pm)
You need to submit a word document file to Turnitin which shows all computer outputs and
discussion. You do not need to submit the dataset.
4 MARKING CRITERIA
Students are advised to read the marking ru
ic provided on Moodle as well as detailed marking
criteria based on this ru
ic.
5 DEDUCTION, LATE SUBMISSION AND EXTENSION
Late submission penalty: - 5% of the total available marks per calendar day unless an extension is
approved. This means 0.75 marks (out of 15 marks) per day.
For extension application procedure, please refer to Section 3.3 of the Subject Outline. Please do
NOT email the lecturer or tutor to seek an extension, you need to follow the procedure described in
the Subject Outline.
6 PLAGIARISM
Please read Section 3.4 Plagiarism and Referencing, from the Subject Outline. Below is part of the
statement:
“Students plagiarising run the risk of severe penalties ranging from a reduction through to 0 marks for a first
offence for a single assessment task, to exclusion from KOI in the most serious repeat cases. Exclusion has
serious visa implications.”
“Authorship is also an issue under Plagiarism – KOI expects students to submit their own original work in both
assessment and exams, or the original work of their group in the case of a group project. All students agree to a
statement of authorship when submitting assessments online via Moodle, stating that the work submitted is
their own original work.
The following are examples of academic misconduct and can attract severe penalties:
• Handing in work created by someone else (without acknowledgement), whether copied from another
student, written by someone else, or from any published or electronic source, is fraud, and falls under
the general Plagiarism guidelines.
• Students who willingly allow another student to copy their work in any assessment may be considered
to assisting in copying/cheating, and similar penalties may be applied. ”
Section 1
DATA SET 1 DESCRIPTION
The data is a secodary data since it is obtained from an already existing source. It was collected from Fair Trading Website (https:
www.fairtrading.nsw.gov.au/about-fair-trading/data-and-statistics
ental-bond-data) and it is a subset of "Rental bond lodgement data 2019." The data has got four variables both categorical and numeric. Postal code and number of bedrooms are categorical varibles of the nominal type since values in them are labels with no significant value. They indicate the postal code of the subu
and number of bedrooms in a dwelling type respectively. Dwelling type is a categorical variable indicating the type of housing and lastly weekly rent is a numeric variable of ratio type (Hinton, XXXXXXXXXXIt represent the weekly rent paid for any type of dwelling.
DATA SET 2 DESCRIPTION
The data is primary data since it was collected directly from an online survey. It was collected by radomly surveying responses from international students in an online poll regarding the subu
s where they dwell. Only 30 responses were randomly picked. The sample has got four variables all of which are categorical. The variables are gender indicating whether the individual was male or female, origin indicating the country of origin, postal code and subu
indicating the place where the student resided. Despite the sample being sufficiently large for statistical analysis, there was a possibility of bias, this is because the online poll had no way to check who participated and it would be easy for a local to impersonate an international and participate in the poll (Freund, 2014).
Section 2
Dwelling Type Part 1: Summary Statistics and Pie Chart
Flat Dwelling Type Frequency Proportion
House Flat 5041 50%
Flat House 3751 38%
Flat Others 336 3%
House Te
ace 677 7%
Flat Unknown 195 2%
Te
ace Total 10000 100%
Te
ace
House
Flat
Flat
House
Flat Part 2
Flat Hypothesis Test
House
House Data
Flat Null Hypothesis XXXXXXXXXXp = 0.5
Flat Level of Significance 0.05
Flat Number of Items of Interest 5041
House Sample Size 10000
Flat
House Intermediate Calculations
Flat Sample Proportion 0.5041
Flat Standard E
or 0.0050
Unknown Z Test Statistic 0.8200
Flat
Unknown Upper-Tail Test
Flat Upper Critical Value 1.6449
Flat p-Value 0.2061
Te
ace Do not reject the null hypothesis
Flat
House
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ace
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